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07/28/2023
VPP: Efficient Conditional 3D Generation via Voxel-Point Progressive Representation
Conditional 3D generation is undergoing a significant advancement, enabl...
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05/31/2023
Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color Contrast
Geometry and color information provided by the point clouds are both cru...
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05/22/2023
Revisiting Data Augmentation in Model Compression: An Empirical and Comprehensive Study
The excellent performance of deep neural networks is usually accompanied...
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04/28/2023
CORSD: Class-Oriented Relational Self Distillation
Knowledge distillation conducts an effective model compression method wh...
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03/08/2023
CLIP-FO3D: Learning Free Open-world 3D Scene Representations from 2D Dense CLIP
Training a 3D scene understanding model requires complicated human annot...
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02/07/2023
Hebbian and Gradient-based Plasticity Enables Robust Memory and Rapid Learning in RNNs
Rapidly learning from ongoing experiences and remembering past events wi...
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02/05/2023
Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
Mainstream 3D representation learning approaches are built upon contrast...
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12/16/2022
Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?
The success of deep learning heavily relies on large-scale data with com...
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11/25/2022
Language-Assisted 3D Feature Learning for Semantic Scene Understanding
Learning descriptive 3D features is crucial for understanding 3D scenes ...
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10/08/2022
LW-ISP: A Lightweight Model with ISP and Deep Learning
The deep learning (DL)-based methods of low-level tasks have many advant...
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07/12/2022
Contrastive Deep Supervision
The success of deep learning is usually accompanied by the growth in neu...
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06/01/2022
YOLoC: DeploY Large-Scale Neural Network by ROM-based Computing-in-Memory using ResiduaL Branch on a Chip
Computing-in-memory (CiM) is a promising technique to achieve high energ...
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06/01/2022
Rethinking the Augmentation Module in Contrastive Learning: Learning Hierarchical Augmentation Invariance with Expanded Views
A data augmentation module is utilized in contrastive learning to transf...
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05/25/2022
Region-aware Knowledge Distillation for Efficient Image-to-Image Translation
Recent progress in image-to-image translation has witnessed the success ...
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05/23/2022
PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D Detection
The remarkable breakthroughs in point cloud representation learning have...
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04/21/2022
Learn from Unpaired Data for Image Restoration: A Variational Bayes Approach
Collecting paired training data is difficult in practice, but the unpair...
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04/14/2022
Unsupervised Deep Learning Meets Chan-Vese Model
The Chan-Vese (CV) model is a classic region-based method in image segme...
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03/23/2022
Chiplet Actuary: A Quantitative Cost Model and Multi-Chiplet Architecture Exploration
Multi-chip integration is widely recognized as the extension of Moore's ...
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03/12/2022
Wavelet Knowledge Distillation: Towards Efficient Image-to-Image Translation
Remarkable achievements have been attained with Generative Adversarial N...
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12/30/2021
Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks
Quantized neural networks typically require smaller memory footprints an...
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11/03/2021
Multi-Glimpse Network: A Robust and Efficient Classification Architecture based on Recurrent Downsampled Attention
Most feedforward convolutional neural networks spend roughly the same ef...
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10/23/2021
AFEC: Active Forgetting of Negative Transfer in Continual Learning
Continual learning aims to learn a sequence of tasks from dynamic data d...
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02/11/2020
PCNN: Pattern-based Fine-Grained Regular Pruning towards Optimizing CNN Accelerators
Weight pruning is a powerful technique to realize model compression. We ...
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01/20/2020
An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices
Weight pruning has been widely acknowledged as a straightforward and eff...
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11/28/2019
Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation
Existing domain adaptation methods aim at learning features that can be ...
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11/27/2019
Exploring Frequency Domain Interpretation of Convolutional Neural Networks
Many existing interpretation methods of convolutional neural networks (C...
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09/06/2019
PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-time Execution on Mobile Devices
Model compression techniques on Deep Neural Network (DNN) have been wide...
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07/03/2019
Non-structured DNN Weight Pruning Considered Harmful
Large deep neural network (DNN) models pose the key challenge to energy ...
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05/28/2019
Brain-inspired reverse adversarial examples
A human does not have to see all elephants to recognize an animal as an ...
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05/17/2019
Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation
Convolutional neural networks have been widely deployed in various appli...
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05/02/2019
Toward Extremely Low Bit and Lossless Accuracy in DNNs with Progressive ADMM
Weight quantization is one of the most important techniques of Deep Neur...
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03/29/2019
